fingerprinted now should work correctly

_paramters.py

@@ -32,7 +32,7 @@ main_arg_parser.add_argument("--data_stretch", type=strtobool, default=True, hel
 
 # Transformation Parameters
 main_arg_parser.add_argument("--data_loudness_ratio", type=float, default=0, help="")  # 0.4
-main_arg_parser.add_argument("--data_shift_ratio", type=float, default=0.3, help="")  # 0.3
+main_arg_parser.add_argument("--data_shift_ratio", type=float, default=0.3, help="")  # 0.4
 main_arg_parser.add_argument("--data_noise_ratio", type=float, default=0, help="")  # 0.4
 main_arg_parser.add_argument("--data_mask_ratio", type=float, default=0, help="")  # 0.2
 main_arg_parser.add_argument("--data_speed_ratio", type=float, default=0, help="")  # 0.3
@@ -40,7 +40,6 @@ main_arg_parser.add_argument("--data_speed_factor", type=float, default=0, help=
 
 # Model Parameters
 main_arg_parser.add_argument("--model_type", type=str, default="RCC", help="")
-main_arg_parser.add_argument("--model_secondary_type", type=str, default="RCC", help="")
 main_arg_parser.add_argument("--model_weight_init", type=str, default="xavier_normal_", help="")
 main_arg_parser.add_argument("--model_activation", type=str, default="leaky_relu", help="")
 main_arg_parser.add_argument("--model_filters", type=str, default="[32, 64, 128, 64]", help="")
@@ -55,6 +54,7 @@ main_arg_parser.add_argument("--train_outpath", type=str, default="output", help
 main_arg_parser.add_argument("--train_version", type=strtobool, required=False, help="")
 # FIXME: Stochastic weight Avaraging is not good, maybe its my implementation?
 main_arg_parser.add_argument("--train_sto_weight_avg", type=strtobool, default=False, help="")
+main_arg_parser.add_argument("--train_weight_decay", type=float, default=1e-7, help="")
 main_arg_parser.add_argument("--train_opt_reset_interval", type=int, default=0, help="")
 main_arg_parser.add_argument("--train_epochs", type=int, default=51, help="")
 main_arg_parser.add_argument("--train_batch_size", type=int, default=300, help="")

datasets/binar_masks.py

@@ -8,7 +8,7 @@ from torch.utils.data import Dataset
 import torch
 
 import variables as V
-from ml_lib.modules.utils import F_x
+from ml_lib.modules.util import F_x
 
 
 class BinaryMasksDataset(Dataset):

main.py

@@ -2,14 +2,13 @@
 # =============================================================================
 from pathlib import Path
 
-from tqdm import tqdm
 import warnings
 
 import torch
 from pytorch_lightning import Trainer
 from pytorch_lightning.callbacks import ModelCheckpoint, EarlyStopping
 
-from ml_lib.modules.utils import LightningBaseModule
+from ml_lib.modules.util import LightningBaseModule
 from ml_lib.utils.logging import Logger
 
 # Project Specific Logger SubClasses
@@ -111,7 +110,7 @@ def run_lightning_loop(config_obj):
                 inference_out = f'{parameters}_test_out.csv'
 
                 from main_inference import prepare_dataloader
-                test_dataloader = prepare_dataloader(config)
+                test_dataloader = prepare_dataloader(config_obj)
 
                 with (outpath / model_type / parameters / version / inference_out).open(mode='w') as outfile:
                     outfile.write(f'file_name,prediction\n')

models/bandwise_conv_classifier.py

@@ -4,7 +4,7 @@ from torch import nn
 from torch.nn import ModuleList
 
 from ml_lib.modules.blocks import ConvModule, LinearModule
-from ml_lib.modules.utils import (LightningBaseModule, HorizontalSplitter, HorizontalMerger)
+from ml_lib.modules.util import (LightningBaseModule, HorizontalSplitter, HorizontalMerger)
 from util.module_mixins import (BaseOptimizerMixin, BaseTrainMixin, BaseValMixin, BinaryMaskDatasetMixin,
                                 BaseDataloadersMixin)
 

models/bandwise_conv_multihead_classifier.py

@@ -5,7 +5,7 @@ from torch import nn
 from torch.nn import ModuleList
 
 from ml_lib.modules.blocks import ConvModule, LinearModule
-from ml_lib.modules.utils import (LightningBaseModule, Flatten, HorizontalSplitter)
+from ml_lib.modules.util import (LightningBaseModule, Flatten, HorizontalSplitter)
 from util.module_mixins import (BaseOptimizerMixin, BaseTrainMixin, BaseValMixin, BinaryMaskDatasetMixin,
                                 BaseDataloadersMixin)
 

models/conv_classifier.py

@@ -4,7 +4,7 @@ from torch import nn
 from torch.nn import ModuleList
 
 from ml_lib.modules.blocks import ConvModule, LinearModule
-from ml_lib.modules.utils import LightningBaseModule
+from ml_lib.modules.util import LightningBaseModule
 from util.module_mixins import (BaseOptimizerMixin, BaseTrainMixin, BaseValMixin, BinaryMaskDatasetMixin,
                                 BaseDataloadersMixin)
 

models/ensemble.py

@@ -5,7 +5,7 @@ import torch
 from torch import nn
 from torch.nn import ModuleList
 
-from ml_lib.modules.utils import LightningBaseModule
+from ml_lib.modules.util import LightningBaseModule
 from ml_lib.utils.config import Config
 from ml_lib.utils.model_io import SavedLightningModels
 from util.module_mixins import (BaseOptimizerMixin, BaseTrainMixin, BaseValMixin, BinaryMaskDatasetMixin,

models/residual_conv_classifier.py

@@ -4,7 +4,7 @@ from torch import nn
 from torch.nn import ModuleList
 
 from ml_lib.modules.blocks import ConvModule, LinearModule, ResidualModule
-from ml_lib.modules.utils import LightningBaseModule
+from ml_lib.modules.util import LightningBaseModule
 from util.module_mixins import (BaseOptimizerMixin, BaseTrainMixin, BaseValMixin, BinaryMaskDatasetMixin,
                                 BaseDataloadersMixin)
 

multi_run.py

@@ -0,0 +1,52 @@
+import shutil
+import warnings
+
+from util.config import MConfig
+
+warnings.filterwarnings('ignore', category=FutureWarning)
+warnings.filterwarnings('ignore', category=UserWarning)
+
+# Imports
+# =============================================================================
+
+from main import run_lightning_loop
+from _paramters import main_arg_parser
+
+
+if __name__ == '__main__':
+
+    args = main_arg_parser.parse_args()
+    # Model Settings
+    config = MConfig().read_namespace(args)
+
+    arg_dict = dict()
+    for seed in range(40, 45):
+        arg_dict.update(main_seed=seed)
+        for model in ['CC', 'BCMC', 'BCC', 'RCC']:
+            arg_dict.update(model_type=model)
+            raw_conf = dict(data_speed_factor=0.0, data_speed_ratio=0.0, data_mask_ratio=0.0,
+                            data_noise_ratio=0.0, data_shift_ratio=0.0, data_loudness_ratio=0.0)
+            all_conf = dict(data_speed_factor=0.7, data_speed_ratio=0.2, data_mask_ratio=0.2,
+                              data_noise_ratio=0.4, data_shift_ratio=0.4, data_loudness_ratio=0.4)
+            speed_conf = dict(data_speed_factor=0.7, data_speed_ratio=0.2, data_mask_ratio=0.0,
+                            data_noise_ratio=0.0, data_shift_ratio=0.0, data_loudness_ratio=0.0)
+            mask_conf = dict(data_speed_factor=0.0, data_speed_ratio=0.0, data_mask_ratio=0.2,
+                             data_noise_ratio=0.0, data_shift_ratio=0.0, data_loudness_ratio=0.0)
+            noise_conf = dict(data_speed_factor=0.0, data_speed_ratio=0.0, data_mask_ratio=0.0,
+                              data_noise_ratio=0.4, data_shift_ratio=0.0, data_loudness_ratio=0.0)
+            shift_conf = dict(data_speed_factor=0.0, data_speed_ratio=0.0, data_mask_ratio=0.0,
+                              data_noise_ratio=0.0, data_shift_ratio=0.4, data_loudness_ratio=0.0)
+            loudness_conf = dict(data_speed_factor=0.0, data_speed_ratio=0.0, data_mask_ratio=0.0,
+                                  data_noise_ratio=0.0, data_shift_ratio=0.0, data_loudness_ratio=0.4)
+
+            for dicts in [raw_conf, all_conf, speed_conf, mask_conf,noise_conf, shift_conf, loudness_conf]:
+
+                arg_dict.update(dicts)
+                config = config.update(arg_dict)
+                version_path = config.exp_path / config.version
+                if version_path.exists():
+                    if not (version_path / 'weights.ckpt').exists():
+                        shutil.rmtree(version_path)
+                    else:
+                        continue
+                run_lightning_loop(config)

util/module_mixins.py

@@ -15,7 +15,7 @@ from torchvision.transforms import Compose, RandomApply
 from ml_lib.audio_toolset.audio_augmentation import Speed
 from ml_lib.audio_toolset.mel_augmentation import NoiseInjection, LoudnessManipulator, ShiftTime, MaskAug
 from ml_lib.audio_toolset.audio_io import AudioToMel, MelToImage, NormalizeLocal
-from ml_lib.modules.utils import LightningBaseModule
+from ml_lib.modules.util import LightningBaseModule
 from ml_lib.utils.transforms import ToTensor
 
 import variables as V
@@ -25,7 +25,7 @@ class BaseOptimizerMixin:
 
     def configure_optimizers(self):
         assert isinstance(self, LightningBaseModule)
-        opt = Adam(params=self.parameters(), lr=self.params.lr, weight_decay=1e-7)
+        opt = Adam(params=self.parameters(), lr=self.params.lr, weight_decay=self.params.weight_decay)
         if self.params.sto_weight_avg:
             opt = SWA(opt, swa_start=10, swa_freq=5, swa_lr=0.05)
         return opt
@@ -181,7 +181,7 @@ class BaseDataloadersMixin(ABC):
     # Validation Dataloader
     def val_dataloader(self):
         assert isinstance(self, LightningBaseModule)
-        val_dataloader = DataLoader(dataset=self.dataset.val_dataset, shuffle=True,
+        val_dataloader = DataLoader(dataset=self.dataset.val_dataset, shuffle=False,
                                     batch_size=self.params.batch_size, num_workers=self.params.worker)
 
         train_dataloader = DataLoader(self.dataset.val_train_dataset, num_workers=self.params.worker,